Our PhD and MS level biostatisticians are highly trained in a range of statistical and analytic methods, including:
- Longitudinal data analysis
- ANOVA, regression, logistic regression
- Bayesian data analyses
- Sample size and power estimation
- Statistical genomics
- Survival analyses
- Principal component and factor analysis
- Path modeling
- Structural equation modeling
- Cluster analysis
- Complex survey data analysis
- Statistical simulations and graphics
- Profile analysis
- Gene expression data analysis
- Mixed effects models
- Generalized Estimating Equations (GEE)
- Propensity Score Matching (PSM)
- Evaluation of medical tests for classification and prediction
- Estimation of median lethal doses (LD50)/quantal dose-response curves
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Biostatisticians & Epidemiologists
Elizabeth Tolley Professor of Biostatistics, Preventive Medicine Dr. Tolley holds a doctorate from Virginia Tech, Blacksburg, VA, and received post-doctoral training in building statistical models at North Carolina State University, Raleigh, NC. As a faculty member at UTHSC, she is course director of the two-semester graduate-level course in Biostatistics for the Health Sciences I and II and for the graduate-level course in Linear Regression Models offered in the Masters of Epidemiology degree program. She has mentored numerous graduate students at the masters and doctoral levels. Dr. Tolley has served as a biostatistician, co-investigator, and consultant on many NIH grants. She also provides biostatistical consulting and collaboration services for many basic science and clinical investigators as part of her assigned faculty duties. Throughout her career, Dr. Tolley has worked with clinical investigators to develop mechanistic or diagnostic models of disease outcomes and predictive models of such outcomes.
Quynh T. Tran Assistant Professor of Biostatistics, Preventive Medicine Quynh earned her PhD in Biology with a concentration in Bioinformatics and MS degrees in Statistics and Bioinformatics from the University of Memphis. Dr. Tran also holds a BS in Computer Science from SUNY at Stony Brook. She has extensive experience working on large-scale biological datasets such as microarray, RNA-seq, and ChIP-seq. She has served as a biostatistician on several clinical studies such as TARGIT (Treating Adults at Risk for weight Gain with Interactive Technology) and CANDLE (Conditions Affecting Neurocognitive Development and Learning in Early Childhood). Dr. Tran is interested in translational research in childhood obesity and eczema. Other areas of research interest include data mining, statistical methods for large-scale epidemiologic data, bioinformatics applications and methods for NGS data.